class_docstr: The LogisticRidgeRegression class is used to fit logistic ridge regression models against one or more labels optimized over a
  provided list of ridge alpha parameters. The optimal ridge alpha value is chosen for each label by minimizing the
  average out of fold log_loss scores.
docstr: Generates GWAS covariates for the target labels in the provided label DataFrame by applying the model resulting
  from the LogisticRidgeRegression fit method to the starting reduced block matrix.
  
  Args:
      response : String specifying the desired output.  Can be 'linear' to specify the direct output of the linear
          WGR model (default) or 'sigmoid' to specify predicted label probabilities.
  
  Returns:
      Pandas DataFrame containing  covariate values. The shape and order match label_df such that the
      rows are indexed by sample ID and the columns by label. The column types are float64.
docstr_tok: ["Generates","GWAS","covariates","for","the","target","labels","in","the","provided","label","DataFrame","by","applying","the","model","resulting","from","the","LogisticRidgeRegression","fit","method","to","the","starting","reduced","block","matrix",".","Args",":","response",":","String","specifying","the","desired","output",".","Can","be","'linear","'","to","specify","the","direct","output","of","the","linear","WGR","model","(","default",")","or","'sigmoid","'","to","specify","predicted","label","probabilities",".","Returns",":","Pandas","DataFrame","containing","covariate","values",".","The","shape","and","order","match","label_df","such","that","the","rows","are","indexed","by","sample","ID","and","the","columns","by","label",".","The","column","types","are","float64","."]
code: def transform(self, response: str = "linear") -> pd.DataFrame:
      """
      Generates GWAS covariates for the target labels in the provided label DataFrame by applying the model resulting
      from the LogisticRidgeRegression fit method to the starting reduced block matrix.
  
      Args:
          response : String specifying the desired output.  Can be 'linear' to specify the direct output of the linear
              WGR model (default) or 'sigmoid' to specify predicted label probabilities.
  
      Returns:
          Pandas DataFrame containing  covariate values. The shape and order match label_df such that the
          rows are indexed by sample ID and the columns by label. The column types are float64.
      """
      _check_model(self.model_df)
      _check_cv(self.cv_df)
  
      block_prediction_df = self.reduce_block_matrix(response)
      self.y_hat_df = flatten_prediction_df(
          block_prediction_df, self.sample_blocks, self._label_df
      )
  
      record_hls_event("wgrLogisticRegressionTransform")
  
      return self.y_hat_df
  
imports: from .logistic_udfs import *
  from .ridge_reduction import RidgeReduction
  from .model_functions import _prepare_labels_and_warn, _prepare_covariates, _check_model, _check_cv, _is_binary
  from nptyping import Float, NDArray
  import pandas
  from pyspark.sql import DataFrame
  from pyspark.sql.functions import pandas_udf, PandasUDFType
  import pyspark.sql.functions
  from typeguard import typechecked
  from typing import Any, Dict, List
  import warnings
  from glow.logging import record_hls_event
label_desc: Use image node_id 14 for calling the LogisticRidgeRegression obj's underlying member method code with example usage: obj.transform(response) and returns: self